Regularized Structural Equation Modeling

Abstract

A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regularization has gained wide adoption in regression, very little has transferred to models with latent variables. By adding penalties to specific parameters in a structural equation model, researchers have a high level of flexibility in reducing model complexity, overcoming poor fitting models, and the creation of models that are more likely to generalize to new samples. The proposed method was evaluated through a simulation study, two illustrative examples involving a measurement model, and one empirical example involving the structural part of the model to demonstrate RegSEM’s utility.

Keywords:

• factor analysis
• lasso
• penalization
• regularization
• ridge
• shrinkage
• structural equation modeling

FUNDING

Ross Jacobucci was supported by funding through the National Institute on Aging Grant Number T32AG0037.

Additional information

Funding

Ross Jacobucci was supported by funding through the National Institute on Aging Grant Number T32AG0037.